Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-27 06:39:25 +01:00
Code Issues Packages Projects Releases Wiki Activity
df270ef745
llama.cpp/examples/batched-bench
History
Georgi Gerganov df270ef745
llama : refactor sampling v2 (#9294) 
- Add `struct llama_sampler` and `struct llama_sampler_i`
- Add `llama_sampler_` API
- Add `llama_sampler_chain_` API for chaining multiple samplers
- Remove `LLAMA_API_INTERNAL`
- Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`
2024-09-07 15:16:19 +03:00
..
batched-bench.cpp llama : refactor sampling v2 (#9294) 2024-09-07 15:16:19 +03:00
CMakeLists.txt build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809) 2024-06-13 00:41:52 +01:00
README.md batched-bench : add --output-format jsonl option (#9293) 2024-09-06 17:59:58 +02:00

README.md

llama.cpp/example/batched-bench

Benchmark the batched decoding performance of llama.cpp

Usage

There are 2 modes of operation:

  • prompt not shared - each batch has a separate prompt of size PP (i.e. N_KV = B*(PP + TG))
  • prompt is shared - there is a common prompt of size PP used by all batches (i.e. N_KV = PP + B*TG)
./llama-batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]

# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
./llama-batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99

# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps

# custom set of batches
./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32

Sample results

  • PP - prompt tokens per batch
  • TG - generated tokens per batch
  • B - number of batches
  • N_KV - required KV cache size
  • T_PP - prompt processing time (i.e. time to first token)
  • S_PP - prompt processing speed ((B*PP)/T_PP or PP/T_PP)
  • T_TG - time to generate all batches
  • S_TG - text generation speed ((B*TG)/T_TG)
  • T - total time
  • S - total speed (i.e. all tokens / total time)
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
128 128 1 256 0.108 1186.64 3.079 41.57 3.187 80.32
128 128 2 512 0.198 1295.19 5.029 50.90 5.227 97.95
128 128 4 1024 0.373 1373.96 6.878 74.44 7.251 141.23
128 128 8 2048 0.751 1363.27 7.344 139.43 8.095 252.99
128 128 16 4096 1.570 1304.68 8.455 242.23 10.024 408.60
128 128 32 8192 3.408 1201.73 8.801 465.40 12.209 670.96
128 256 1 384 0.107 1196.70 6.329 40.45 6.436 59.67
128 256 2 768 0.194 1317.45 10.239 50.00 10.433 73.61
128 256 4 1536 0.366 1399.03 13.960 73.35 14.326 107.22
128 256 8 3072 0.751 1363.92 15.110 135.54 15.861 193.69
128 256 16 6144 1.569 1304.93 18.073 226.64 19.642 312.80
128 256 32 12288 3.409 1201.35 19.223 426.15 22.633 542.93

JSONL output

Pass --output-format jsonl to output JSONL instead of Markdown, á la

{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 1, "n_kv": 256, "t_pp": 0.233810, "speed_pp": 547.453064, "t_tg": 3.503684, "speed_tg": 36.532974, "t": 3.737494, "speed": 68.495094}
{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 2, "n_kv": 512, "t_pp": 0.422602, "speed_pp": 605.770935, "t_tg": 11.106112, "speed_tg": 23.050371, "t": 11.528713, "speed": 44.410854}